NDLI: A hybrid rough sets and support vector regression approach to short-term electricity load forecasting

Content Provider	IEEE Xplore Digital Library
Author	Fang Ruiming
Copyright Year	2008
Description	Author affiliation: Dept. of Electr. Eng., Nat. Huaqiao Univ., Quanzhou (Fang Ruiming)
Abstract	This paper aims to develop a load forecasting method for short-term load forecasting based on a hybrid approach, which combines the support vector regression method and the rough sets method. In the first stage, the rough sets method is applied to reduce the redundant attributes among varied factors that affect the short-term load forecasting. Then, a SVR module is trained using historical data reconstructed according to the attribution reduction results obtained by the first stage to perform the forecast. Numerical experiments on the historical data of Liaoning province grid in China show that, when compared against both neural network method and standard SVR method, the proposed method can forecast more accurate results while enhancing the training speed.
Starting Page	1
Ending Page	5
File Size	582280
Page Count	5
File Format	PDF
ISBN	9781424419050
ISSN	19325517
DOI	10.1109/PES.2008.4596688
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Load forecasting Load modeling Training Support vector machines Artificial neural networks Forecasting Meteorology support vector regression attribution reduction load forecasting rough sets
Content Type	Text
Resource Type	Article

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